OpenRaiser/Intern-Atlas huggingface dataset:
Data Augmentation of Wearable Sensor Data for Parkinson's Disease Monitoring using Convolutional Neural Networks
Venue: arXiv cs.CV 2017
Year: 2017

While convolutional neural networks (CNNs) have been successfully applied to many challenging classification applications, they typically require large datasets for training. When the availability of labeled data is limited, data augmentation is a critical preprocessing step for CNNs. However, data augmentation for wearable sensor data has not been deeply investigated yet. In this paper, various data augmentation methods for wearable sensor data are proposed. The proposed methods and CNNs are applied to the classification of the motor state of Parkinson's Disease patients, which is challenging due to small dataset size, noisy labels, and large intra-class variability. Appropriate augmentation improves the classification performance from 77.54\% to 86.88\%.
Dataflow Matrix Machines as a Model of Computations with Linear Streams
Venue: arXiv cs.NE 2017
Year: 2017

We overview dataflow matrix machines as a Turing complete generalization of recurrent neural networks and as a programming platform. We describe vector space of finite prefix trees with numerical leaves which allows us to combine expressive power of dataflow matrix machines with simplicity of traditional recurrent neural networks.
Hashtag-centric Immersive Search on Social Media
Venue: arXiv cs.IR 2017
Year: 2017

Social media information distributes in different Online Social Networks (OSNs). This paper addresses the problem integrating the cross-OSN information to facilitate an immersive social media search experience. We exploit hashtag, which is widely used to annotate and organize multi-modal items in different OSNs, as the bridge for information aggregation and organization. A three-stage solution framework is proposed for hashtag representation, clustering and demonstration. Given an event query, the related items from three OSNs, Twitter, Flickr and YouTube, are organized in cluster-hashtag-item hierarchy for display. The effectiveness of the proposed solution is validated by qualitative and quantitative experiments on hundreds of trending event queries.
Efficient Detection of Points of Interest from Georeferenced Visual Content
Venue: arXiv cs.DB 2017
Year: 2017

Many people take photos and videos with smartphones and more recently with 360-degree cameras at popular places and events, and share them in social media. Such visual content is produced in large volumes in urban areas, and it is a source of information that online users could exploit to learn what has got the interest of the general public on the streets of the cities where they live or plan to visit. A key step to providing users with that information is to identify the most popular k spots in specified areas. In this paper, we propose a clustering and incremental sampling (C&IS) approach that trades off accuracy of top-k results for detection speed. It uses clustering to determine areas with high density of visual content, and incremental sampling, controlled by stopping criteria, to limit the amount of computational work. It leverages spatial metadata, which represent the scenes in the visual content, to rapidly detect the hotspots, and uses a recently proposed Gaussian probability model to describe the capture intention distribution in the query area. We evaluate the approach with metadata, derived from a non-synthetic, user-generated dataset, for regular mobile and 360-degree visual content. Our results show that the C&IS approach offers 2.8x-19x reductions in processing time over an optimized baseline, while in most cases correctly identifying 4 out of 5 top locations.
Situational Awareness with PMUs and SCADA
Venue: arXiv cs.SY 2017
Year: 2017

Phasor measurement units (PMUs) are integrated to the transmission networks under the smart grid umbrella. The observability of PMUs is geographically limited due to their high cost in integration. The measurements of PMUs can be complemented by those from widely installed supervisory control and data acquisition (SCADA) to enhance the situational awareness. This paper proposes a new state estimation method that simultaneously integrate both measurements, and show an outstanding performance.
A Construction for Balancing Non-Binary Sequences Based on Gray Code Prefixes
Venue: arXiv cs.IT 2017
Year: 2017

We introduce a new construction for the balancing of non-binary sequences that make use of Gray codes for prefix coding. Our construction provides full encoding and decoding of sequences, including the prefix. This construction is based on a generalization of Knuth's parallel balancing approach, which can handle very long information sequences. However, the overall sequence composed of the information sequence, together with the prefix must be balanced. This is reminiscent of Knuth's serial algorithm. The encoding of our construction does not make use of lookup tables, while the decoding process is simple and can be done in parallel.
Task-specific Word Identification from Short Texts Using a Convolutional Neural Network
Venue: arXiv cs.CL 2017
Year: 2017

Task-specific word identification aims to choose the task-related words that best describe a short text. Existing approaches require well-defined seed words or lexical dictionaries (e.g., WordNet), which are often unavailable for many applications such as social discrimination detection and fake review detection. However, we often have a set of labeled short texts where each short text has a task-related class label, e.g., discriminatory or non-discriminatory, specified by users or learned by classification algorithms. In this paper, we focus on identifying task-specific words and phrases from short texts by exploiting their class labels rather than using seed words or lexical dictionaries. We consider the task-specific word and phrase identification as feature learning. We train a convolutional neural network over a set of labeled texts and use score vectors to localize the task-specific words and phrases. Experimental results on sentiment word identification show that our approach significantly outperforms existing methods. We further conduct two case studies to show the effectiveness of our approach. One case study on a crawled tweets dataset demonstrates that our approach can successfully capture the discrimination-related words/phrases. The other case study on fake review detection shows that our approach can identify the fake-review words/phrases.
Sparse Rational Function Interpolation with Finitely Many Values for the Coefficients
Venue: arXiv cs.SC 2017
Year: 2017

In this paper, we give new sparse interpolation algorithms for black box univariate and multivariate rational functions h=f/g whose coefficients are integers with an upper bound. The main idea is as follows: choose a proper integer beta and let h(beta) = a/b with gcd(a,b)=1. Then f and g can be computed by solving the polynomial interpolation problems f(beta)=ka and g(beta)=ka for some integer k. It is shown that the univariate interpolation algorithm is almost optimal and multivariate interpolation algorithm has low complexity in T but the data size is exponential in n.
A Survey on Security and Privacy Issues of Bitcoin
Venue: arXiv cs.CR 2017
Year: 2017

Bitcoin is a popular cryptocurrency that records alltransactions in a distributed append-only public ledger calledblockchain. The security of Bitcoin heavily relies on the incentive-compatible proof-of-work (PoW) based distributed consensus pro-tocol, which is run by network nodes called miners. In exchangefor the incentive, the miners are expected to honestly maintainthe blockchain. Since its launch in 2009, Bitcoin economy hasgrown at an enormous rate, and it is now worth about 170 billions of dollars. This exponential growth in the market valueof Bitcoin motivates adversaries to exploit weaknesses for profit,and researchers to discover new vulnerabilities in the system,propose countermeasures, and predict upcoming trends.In this paper, we present a systematic survey that covers thesecurity and privacy aspects of Bitcoin. We start by presenting anoverview of the Bitcoin protocol and its major components alongwith their functionality and interactions within the system. Wereview the existing vulnerabilities in Bitcoin and its underlyingmajor technologies such as blockchain and PoW based consensusprotocol. These vulnerabilities lead to the execution of varioussecurity threats to the normal functionality of Bitcoin. Wethen discuss the feasibility and robustness of the state-of-the-art security solutions. Additionally, we present current privacyand anonymity considerations in Bitcoin and discuss the privacy-related threats to Bitcoin users along with the analysis of theexisting privacy-preserving solutions. Finally, we summarize thecritical open challenges and suggest directions for future researchtowards provisioning stringent security and privacy techniquesfor Bitcoin.
Extending Partial Representations of Unit Circular-arc Graphs
Venue: arXiv cs.DM 2017
Year: 2017

The partial representation extension problem, introduced by Klavík et al. (2011), generalizes the recognition problem. In this short note we show that this problem is NP-complete for unit circular-arc graphs.
Nonconvex penalties with analytical solutions for one-bit compressive sensing
Venue: arXiv cs.LG 2017
Year: 2017

One-bit measurements widely exist in the real world, and they can be used to recover sparse signals. This task is known as the problem of learning halfspaces in learning theory and one-bit compressive sensing (1bit-CS) in signal processing. In this paper, we propose novel algorithms based on both convex and nonconvex sparsity-inducing penalties for robust 1bit-CS. We provide a sufficient condition to verify whether a solution is globally optimal or not. Then we show that the globally optimal solution for positive homogeneous penalties can be obtained in two steps: a proximal operator and a normalization step. For several nonconvex penalties, including minimax concave penalty (MCP), $\ell_0$ norm, and sorted $\ell_1$ penalty, we provide fast algorithms for finding the analytical solutions by solving the dual problem. Specifically, our algorithm is more than $200$ times faster than the existing algorithm for MCP. Its efficiency is comparable to the algorithm for the $\ell_1$ penalty in time, while its performance is much better. Among these penalties, the sorted $\ell_1$ penalty is most robust to noise in different settings.
Distributed Contingency Analysis over Wide Area Network among Dispatch Centers
Venue: arXiv cs.DC 2017
Year: 2017

Traditionally, a regional dispatch center uses the equivalent method to deal with external grids, which fails to reflect the interactions among regions. This paper proposes a distributed N-1 contingency analysis (DCA) solution, where dispatch centers join a coordinated computation using their private data and computing resources. A distributed screening method is presented to determine the Critical Contingency Set (DCCS) in DCA. Then, the distributed power flow is formulated as a set of boundary equations, which is solved by a Jacobi-Free Newton-GMRES (JFNG) method. During solving the distributed power flow, only boundary conditions are exchanged. Acceleration techniques are also introduced, including reusing preconditioners and optimal resource scheduling during parallel processing of multiple contingencies. The proposed method is implemented on a real EMS platform, where tests using the Southwest Regional Grid of China are carried out to validate its feasibility.
Optimal Relaying in Energy Harvesting Wireless Networks with Wireless-Powered Relays
Venue: arXiv cs.NI 2017
Year: 2017

In this paper, we consider a wireless cooperative network with an energy harvesting relay which is powered by the energy harvested from ambient RF waves, such as that of a data packet. At any given time, the relay operates either in the energy harvesting (EH) mode or the data decoding (DD) mode, but not both. Separate energy and data buffers are kept at the relay to store the harvested energy and decoded data packets, respectively. In this paper, we optimize a time switching policy that switches between the EH mode and DD mode to maximize the system throughput or minimize the average transmission delay. Both static and dynamic time switching policies are derived. In particular, static policies are the ones where EH or DD mode is selected with a pre-determined probability. In contrast, in a dynamic policy, the mode is selected dynamically according to the states of data and energy buffers. We prove that the throughput-optimal static and dynamic policies keep the relay data buffer at the boundary of stability. More specifically, we show that the throughput-optimal dynamic policy has a threshold-based structure. Moreover, we prove that the delay-optimal dynamic policy also has a threshold-based structure and keeps at most one packet at the relay. We notice that the delay-optimal and throughput-optimal dynamic policies coincide in most cases. However, it is not true for optimal static policies. Finally, through extensive numerical results, we show the efficiency of optimal dynamic policies compared with the static ones in different conditions.
CRNN: A Joint Neural Network for Redundancy Detection
Venue: arXiv cs.CL 2017
Year: 2017

This paper proposes a novel framework for detecting redundancy in supervised sentence categorisation. Unlike traditional singleton neural network, our model incorporates character-aware convolutional neural network (Char-CNN) with character-aware recurrent neural network (Char-RNN) to form a convolutional recurrent neural network (CRNN). Our model benefits from Char-CNN in that only salient features are selected and fed into the integrated Char-RNN. Char-RNN effectively learns long sequence semantics via sophisticated update mechanism. We compare our framework against the state-of-the-art text classification algorithms on four popular benchmarking corpus. For instance, our model achieves competing precision rate, recall ratio, and F1 score on the Google-news data-set. For twenty-news-groups data stream, our algorithm obtains the optimum on precision rate, recall ratio, and F1 score. For Brown Corpus, our framework obtains the best F1 score and almost equivalent precision rate and recall ratio over the top competitor. For the question classification collection, CRNN produces the optimal recall rate and F1 score and comparable precision rate. We also analyse three different RNN hidden recurrent cells' impact on performance and their runtime efficiency. We observe that MGU achieves the optimal runtime and comparable performance against GRU and LSTM. For TFIDF based algorithms, we experiment with word2vec, GloVe, and sent2vec embeddings and report their performance differences.
A Random-Fern based Feature Approach for Image Matching
Venue: arXiv cs.CV 2017
Year: 2017

Image or object recognition is an important task in computer vision. With the hight-speed processing power on modern platforms and the availability of mobile phones everywhere, millions of photos are uploaded to the internet per minute, it is critical to establish a generic framework for fast and accurate image processing for automatic recognition and information retrieval. In this paper, we proposed an efficient image recognition and matching method that is originally derived from Naive Bayesian classification method to construct a probabilistic model. Our method support real-time performance and have very high ability to distinguish similar images with high details. Experiments are conducted together with intensive comparison with state-of-the-arts on image matching, such as Ferns recognition and SIFT recognition. The results demonstrate satisfactory performance.
One-step and Two-step Classification for Abusive Language Detection on Twitter
Venue: arXiv cs.CL 2017
Year: 2017

Automatic abusive language detection is a difficult but important task for online social media. Our research explores a two-step approach of performing classification on abusive language and then classifying into specific types and compares it with one-step approach of doing one multi-class classification for detecting sexist and racist languages. With a public English Twitter corpus of 20 thousand tweets in the type of sexism and racism, our approach shows a promising performance of 0.827 F-measure by using HybridCNN in one-step and 0.824 F-measure by using logistic regression in two-steps.
The Singularity May Be Near
Venue: arXiv cs.AI 2017
Year: 2017

Toby Walsh in 'The Singularity May Never Be Near' gives six arguments to support his point of view that technological singularity may happen but that it is unlikely. In this paper, we provide analysis of each one of his arguments and arrive at similar conclusions, but with more weight given to the 'likely to happen' probability.
Submanifold Sparse Convolutional Networks
Venue: arXiv cs.NE 2017
Year: 2017

Convolutional network are the de-facto standard for analysing spatio-temporal data such as images, videos, 3D shapes, etc. Whilst some of this data is naturally dense (for instance, photos), many other data sources are inherently sparse. Examples include pen-strokes forming on a piece of paper, or (colored) 3D point clouds that were obtained using a LiDAR scanner or RGB-D camera. Standard "dense" implementations of convolutional networks are very inefficient when applied on such sparse data. We introduce a sparse convolutional operation tailored to processing sparse data that differs from prior work on sparse convolutional networks in that it operates strictly on submanifolds, rather than "dilating" the observation with every layer in the network. Our empirical analysis of the resulting submanifold sparse convolutional networks shows that they perform on par with state-of-the-art methods whilst requiring substantially less computation.
Neuroevolution on the Edge of Chaos
Venue: arXiv cs.NE 2017
Year: 2017

Echo state networks represent a special type of recurrent neural networks. Recent papers stated that the echo state networks maximize their computational performance on the transition between order and chaos, the so-called edge of chaos. This work confirms this statement in a comprehensive set of experiments. Furthermore, the echo state networks are compared to networks evolved via neuroevolution. The evolved networks outperform the echo state networks, however, the evolution consumes significant computational resources. It is demonstrated that echo state networks with local connections combine the best of both worlds, the simplicity of random echo state networks and the performance of evolved networks. Finally, it is shown that evolution tends to stay close to the ordered side of the edge of chaos.
Yeah, Right, Uh-Huh: A Deep Learning Backchannel Predictor
Venue: arXiv cs.CL 2017
Year: 2017

Using supporting backchannel (BC) cues can make human-computer interaction more social. BCs provide a feedback from the listener to the speaker indicating to the speaker that he is still listened to. BCs can be expressed in different ways, depending on the modality of the interaction, for example as gestures or acoustic cues. In this work, we only considered acoustic cues. We are proposing an approach towards detecting BC opportunities based on acoustic input features like power and pitch. While other works in the field rely on the use of a hand-written rule set or specialized features, we made use of artificial neural networks. They are capable of deriving higher order features from input features themselves. In our setup, we first used a fully connected feed-forward network to establish an updated baseline in comparison to our previously proposed setup. We also extended this setup by the use of Long Short-Term Memory (LSTM) networks which have shown to outperform feed-forward based setups on various tasks. Our best system achieved an F1-Score of 0.37 using power and pitch features. Adding linguistic information using word2vec, the score increased to 0.39.
Performance Modeling and Prediction for Dense Linear Algebra
Venue: arXiv cs.PF 2017
Year: 2017

This dissertation introduces measurement-based performance modeling and prediction techniques for dense linear algebra algorithms. As a core principle, these techniques avoid executions of such algorithms entirely, and instead predict their performance through runtime estimates for the underlying compute kernels. For a variety of operations, these predictions allow to quickly select the fastest algorithm configurations from available alternatives. We consider two scenarios that cover a wide range of computations: To predict the performance of blocked algorithms, we design algorithm-independent performance models for kernel operations that are generated automatically once per platform. For various matrix operations, instantaneous predictions based on such models both accurately identify the fastest algorithm, and select a near-optimal block size. For performance predictions of BLAS-based tensor contractions, we propose cache-aware micro-benchmarks that take advantage of the highly regular structure inherent to contraction algorithms. At merely a fraction of a contraction's runtime, predictions based on such micro-benchmarks identify the fastest combination of tensor traversal and compute kernel.
Solver composition across the PDE/linear algebra barrier
Venue: arXiv cs.MS 2017
Year: 2017

The efficient solution of discretisations of coupled systems of partial differential equations (PDEs) is at the core of much of numerical simulation. Significant effort has been expended on scalable algorithms to precondition Krylov iterations for the linear systems that arise. With few exceptions, the reported numerical implementation of such solution strategies is specific to a particular model setup, and intimately ties the solver strategy to the discretisation and PDE, especially when the preconditioner requires auxiliary operators. In this paper, we present recent improvements in the Firedrake finite element library that allow for straightforward development of the building blocks of extensible, composable preconditioners that decouple the solver from the model formulation. Our implementation extends the algebraic composability of linear solvers offered by the PETSc library by augmenting operators, and hence preconditioners, with the ability to provide any necessary auxiliary operators. Rather than specifying up front the full solver configuration, tied to the model, solvers can be developed independently of model formulation and configured at runtime. We illustrate with examples from incompressible fluids and temperature-driven convection.